Objective:

To evaluate the current capabilities and readiness of AI tools in clinical pathology, particularly in image-based diagnostics.

Approach:

Key Findings:

• AI significantly reduces review time in clinical microbiology by screening negative slides.
• AI is viewed as augmented intelligence, supporting rather than replacing human expertise.
• Integration into existing workflows is crucial for clinical adoption of AI tools.

Interpretation:

AI tools are not yet fully autonomous but can enhance diagnostic efficiency and quality in pathology, especially under workforce constraints.

Limitations:

• Current AI tools may not be fully validated for diverse real-world conditions.
• Pathologists face challenges in defining ground truth due to evolving AI capabilities.

Conclusion:

AI has the potential to transform pathology by improving efficiency and diagnostic accuracy, but careful evaluation and integration are essential for clinical readiness.

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This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.